CN116304759A - Vehicle derailment early warning method and device - Google Patents
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Abstract
The invention discloses a vehicle derailment early warning method and device, wherein the method comprises the following steps: establishing a derailment early warning model based on historical displacement data of the sampling machine trolley and the sampling machine cart; determining derailment risk levels of the sampling machine trolley and the sampling machine cart based on the model; when the derailment risk level is not in a low risk level, identifying the derailment condition of the sampling machine trolley or the sampling machine trolley in real time based on wheel vibration signals of the sampling machine trolley and the sampling machine trolley; when the trolley of the sampling machine or the trolley of the sampling machine is identified to be in a derailment state, an alarm signal is sent; and based on the derailment condition of the sampling machine trolley or the sampling machine cart, a safety isolation area is established, so that timely early warning is realized on the derailment risk of the sampling machine cart trolley, an alarm signal is sent out when the derailment condition occurs, the safety isolation area is established, and the safety and the sampling efficiency of sampling work of the sampling machine are improved.
Description
Technical Field
The application relates to the technical field of coal yard sampling, in particular to a vehicle derailment early warning method and device.
Background
In the process of collecting and preparing the coal sample, the collection of the coal sample is an important link in the collecting and preparing process, and whether the coal sample is representative or not is critical. When in manual sampling, the coal mining sample is often influenced by sampling tools, human factors and the like, the representativeness of the coal mining sample is not very strong, and an automatic mechanical sampling machine is used for replacing manual sampling, so that the national standard requirement is completely met. The sampling time, the number of subsamples, the weight of subsamples and the like are ensured, and the adoption of the full section of the cross-section coal flow is ensured, so that the coal sample is representative. Meanwhile, the potential safety hazard of manual sampling is eliminated, the labor intensity of workers is reduced, the influence of human factors and the like is solved, the accuracy of product quality inspection is improved, the quality of coal products is ensured for guiding production in time, and the important effect of timely, accurate and representative coal quality technical inspection is exerted.
At present, a sampling machine system generally comprises sampling machine cart trolleys which are connected with each other and run according to a preset track, and sampling work is completed, but due to the fact that derailment phenomenon occurs in the cart trolleys in the sampling process, the sampling work is affected, the sampling efficiency is improved, potential safety hazards are provided, and in the prior art, the sampling machine system generally only needs to be manually processed when the sampling machine cart trolleys are derailed, and the efficiency is low.
Therefore, how to provide a vehicle derailment early warning method and device, which can detect the running condition of the sampler vehicle in real time and early warn the derailment phenomenon in time, is a technical problem to be solved at present.
Disclosure of Invention
The invention provides a vehicle derailment early warning method and device, which are used for solving the technical problem that the prior art cannot early warn the derailment condition of a large and small travelling crane of a sampling machine in time, so that the sampling work efficiency is low, and the method comprises the following steps:
establishing a derailment early warning model based on historical displacement data of the sampling machine trolley and the sampling machine cart, wherein the historical displacement data comprises displacement information during normal running and displacement information during derailment state;
determining derailment risk levels of the sampling machine trolley and the sampling machine trolley based on the model, wherein the derailment risk levels comprise a low risk level, a medium risk level and a high risk level;
when the derailment risk level is not in a low risk level, identifying the derailment condition of the sampling machine trolley or the sampling machine trolley in real time based on wheel vibration signals of the sampling machine trolley and the sampling machine trolley;
when the trolley of the sampling machine or the trolley of the sampling machine is identified to be in a derailment state, an alarm signal is sent;
and establishing a safe isolation area based on the derailment condition of the sampling machine trolley or the sampling machine cart.
In some embodiments, the derailment early warning model is established based on the historical displacement data of the sampling machine trolley and the sampling machine cart, and specifically comprises the following steps:
s1, clustering the acquired historical displacement data of the sampling machine trolley and the sampling machine cart respectively to obtain the number N1 of clustering sets of the sampling machine trolley and the number N2 of clustering sets of the sampling machine cart;
s2, selecting N1 data from historical displacement data of the trolley of the sampling machine as a first centroid K1, and selecting N1 data from historical displacement data of the trolley of the sampling machine as a second centroid K2;
s3, clustering each historical displacement data of the sampling machine trolley to a set to which a first centroid corresponding to a first distance belongs to obtain N1 aggregated sets, and clustering each historical displacement data of the sampling machine trolley to a set to which a second centroid corresponding to a second distance belongs to obtain N2 aggregated sets, wherein the first distance is the minimum distance between each historical displacement data of the sampling machine trolley and the first centroid, and the second distance is the minimum distance between each historical displacement data of the sampling machine trolley and the second centroid;
s4, taking the central data of each set of the trolley aggregate of the sampling machine as a third mass center K3, taking the central data of each set of the trolley aggregate of the sampling machine as a fourth mass center K4, and comparing a third distance between K1 and K3 and a fourth distance between K2 and K4;
s5, when any one of the third distance and the fourth distance is larger than a preset threshold, repeating the steps S2-S4 until the third distance and the fourth distance are smaller than the preset threshold, and outputting a clustering boundary and an operation state;
s6, determining a derailment threshold based on the cluster boundary, and determining a derailment risk level based on the running state;
and S7, generating a derailment early warning model based on the derailment threshold value and the derailment risk level.
In some of these specific embodiments, determining the derailment risk level of the sampling machine cart and the sampling machine cart based on the model is specifically:
when the real-time running data of the wheels of the large sampling machine vehicle and the small sampling machine vehicle do not exceed a first risk threshold, the coal yard sampling machine system is determined to be in a low risk level;
when the actual running data of the wheels of the sampling machine cart does not exceed the first risk threshold value and the actual running data of the wheels of the sampling machine cart exceeds the first risk threshold value but does not exceed the second risk threshold value, the coal yard sampling machine system is determined to be in a medium risk level;
when the actual running data of the wheels of the sampling machine trolley does not exceed the first risk threshold value and the actual running data of the wheels of the sampling machine trolley does not exceed the first risk threshold value but does not exceed the second risk threshold value, the coal yard sampling machine system is determined to be in a medium risk level;
when the actual running data of the wheels of the sampling machine trolley and the sampling machine cart exceed the first risk threshold value but do not exceed the second risk threshold value, the coal yard sampling machine system is determined to be in a medium risk level;
when the actual running data of the wheels of the sampling machine cart exceeds a first risk threshold value but does not exceed a second risk threshold value and the actual running data of the wheels of the sampling machine cart exceeds the second risk threshold value, the coal yard sampling machine system is determined to be in a high risk level;
when the actual running data of the wheels of the sampling machine trolley exceeds the first risk threshold value but does not exceed the second risk threshold value and the actual running data of the wheels of the sampling machine trolley exceeds the second risk threshold value, the coal yard sampling machine system is determined to be in a high risk level;
and when the actual running data of the wheels of the sampling machine trolley and the sampling machine cart exceed the second risk threshold, the coal yard sampling machine system is determined to be in a high risk level.
In some specific embodiments thereof, S6, determining a derailment threshold based on the cluster boundary, and determining a derailment risk level based on the running state, specifically:
determining a first risk threshold and a second risk threshold according to the clustering boundary, wherein the second risk threshold is larger than the first risk threshold;
when the historical displacement data in the clustered collection of the sampling machine cart and the sampling machine cart are the historical displacement data in normal operation, the derailment risk level is a low risk level;
when the historical displacement data in the clustered collection of the sampling machine cart and the sampling machine cart comprises the historical displacement data in normal operation and the historical displacement data in derailment, the derailment risk level is a medium risk level;
when the historical displacement data in the clustered collection of the sampling machine cart and the sampling machine cart are the historical displacement data in derailment, the derailment risk level is a high risk level.
In some embodiments, when the derailment risk level is not at a low risk level, the derailment condition of the sampling machine trolley or the sampling machine cart is identified in real time based on the wheel vibration signals of the sampling machine trolley and the sampling machine cart, specifically:
acquiring the wheel vibration signal through a sensor arranged on a vehicle rail;
carrying out resonance demodulation on the wheel vibration signals to obtain demodulation signals, intercepting the resonance demodulation signals of a plurality of sleeper period time lengths by taking the time of obtaining the demodulation signals as a starting point, and obtaining the sample to be analyzed;
performing fast Fourier transform on the sample to be analyzed to obtain the corresponding frequency spectrum;
and when the characteristic spectral lines corresponding to sleeper intervals exist in the frequency spectrum, judging that the large or small sampling machine vehicle has derailment.
In some specific embodiments, the safety isolation area is established based on the derailment condition of the sampling machine trolley or the sampling machine cart, specifically:
acquiring derailment information of the sampling machine cart or the sampling machine cart, wherein the derailment information comprises a vehicle number, a track number, a derailment section, a running direction and a running speed during derailment;
and establishing a safety isolation area according to the running directions of the sampling machine cart and the track number and the derailment section.
In some embodiments, the safety isolation area is established according to the traveling directions of the large sampling machine truck and the small sampling machine truck and the track number and the derailment section, specifically:
taking the running directions of the large sampling machine vehicle and the small sampling machine vehicle as forward directions, and setting the tracks within a preset distance of the forward directions of the large sampling machine vehicle and the small sampling machine vehicle as forward safe isolation areas;
and taking the opposite direction of the traveling directions of the large sampling machine vehicle and the small sampling machine vehicle as the opposite direction, and setting the track within the preset distance of the opposite directions of the large sampling machine vehicle and the small sampling machine vehicle as an opposite safety isolation area.
In some embodiments, the method further comprises:
when the sampling machine cart or trolley is derailed, reporting the derailment information and the safety isolation area to a controller;
the safe isolation area is issued to other sampling machine carts and sampling machine carts through the controller;
when other sampling machine carts or sampling machine carts enter the safety isolation area, the sampling machine carts or sampling machine carts entering the safety isolation area are braked emergently through the controller.
In some of these embodiments, determining the derailment risk level of the sampling locomotive and the sampling locomotive based on the model further comprises:
when the derailment risk level of the sampling machine trolley or the sampling machine cart is a high risk level, emergency braking is carried out on the sampling machine trolley or the sampling machine cart through a controller, and the machine is stopped for maintenance.
Correspondingly, the invention also provides a vehicle derailment early warning device, which comprises:
the model building module is used for building a derailment early warning model based on the historical displacement data of the sampling machine trolley and the sampling machine cart, wherein the historical displacement data comprises displacement information during normal running and displacement information during a derailment state;
the determining module is used for determining derailment risk levels of the sampling machine trolley and the sampling machine cart based on the model, wherein the derailment risk levels comprise a low risk level, a medium risk level and a high risk level;
the identifying module is used for identifying the derailment condition of the sampling machine trolley or the sampling machine trolley in real time based on the wheel vibration signals of the sampling machine trolley and the sampling machine trolley when the derailment risk level is not at the low risk level;
the alarm module is used for sending an alarm signal when the sampling machine trolley or the sampling machine cart is identified to be in a derailment state;
the establishing module is used for establishing a safe isolation area based on the derailment condition of the sampling machine trolley or the sampling machine cart.
By applying the technical scheme, a derailment early warning model is established based on the historical displacement data of the sampling machine trolley and the sampling machine cart, wherein the historical displacement data comprises displacement information during normal running and displacement information during derailment state; determining derailment risk levels of the sampling machine trolley and the sampling machine trolley based on the model, wherein the derailment risk levels comprise a low risk level, a medium risk level and a high risk level; when the derailment risk level is not in a low risk level, identifying the derailment condition of the sampling machine trolley or the sampling machine trolley in real time based on wheel vibration signals of the sampling machine trolley and the sampling machine trolley; when the trolley of the sampling machine or the trolley of the sampling machine is identified to be in a derailment state, an alarm signal is sent; and based on the derailment condition of the sampling machine trolley or the sampling machine cart, a safety isolation area is established, so that timely early warning is realized on the derailment risk of the sampling machine cart trolley, an alarm signal is sent out when the derailment condition occurs, the safety isolation area is established, and the safety and the sampling efficiency of sampling work of the sampling machine are improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the description of the embodiments will be briefly introduced below, it being obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 shows a schematic flow chart of a vehicle derailment early warning method according to an embodiment of the present invention;
fig. 2 shows a schematic structural diagram of a vehicle derailment early warning device according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all, of the embodiments of the present application. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.
As shown in fig. 1, the application provides a vehicle derailment early warning method, which is applied to a coal yard sampling machine system comprising a sampling machine trolley and a sampling machine cart, wherein the sampling machine trolley and the sampling machine cart are connected with each other and run according to a preset track, and the method comprises the following steps:
step S101, a derailment early warning model is established based on historical displacement data of the sampling machine trolley and the sampling machine large trolley, wherein the historical displacement data comprises displacement information during normal running and displacement information during derailment state.
In this embodiment, the sampling machine trolley and the sampling machine cart are provided with sensors, and the optional sensors may be composite sensors for measuring various data, or may be sensors acting independently, including sensors for measuring running data of the sampling machine trolley, such as a vibration sensor and a speed sensor.
In this embodiment, the historical displacement data of the sampling machine trolley and the sampling machine cart are collected at fixed time, the collection period can be set according to actual needs, and a derailment early warning model is built according to the collected historical displacement data.
In the present embodiment, the history displacement data should include history displacement data of normal running and history displacement data in a derailed state, while the data amount of history displacement data in a derailed state should not be too low.
In this embodiment, after the derailment early warning model is established, the derailment early warning model is updated and corrected periodically by the collected fresh history displacement data, so that the accuracy of the derailment early warning model is improved.
In order to accurately establish a derailment early warning model, another embodiment of the present application provides a method for establishing a derailment early warning model based on historical displacement data of the sampling machine trolley and the sampling machine cart, specifically:
s1, clustering the acquired historical displacement data of the sampling machine trolley and the sampling machine cart respectively to obtain the number N1 of clustering sets of the sampling machine trolley and the number N2 of clustering sets of the sampling machine cart;
s2, selecting N1 data from historical displacement data of the trolley of the sampling machine as a first centroid K1, and selecting N1 data from historical displacement data of the trolley of the sampling machine as a second centroid K2;
s3, clustering each historical displacement data of the sampling machine trolley to a set to which a first centroid corresponding to a first distance belongs to obtain N1 aggregated sets, and clustering each historical displacement data of the sampling machine trolley to a set to which a second centroid corresponding to a second distance belongs to obtain N2 aggregated sets, wherein the first distance is the minimum distance between each historical displacement data of the sampling machine trolley and the first centroid, and the second distance is the minimum distance between each historical displacement data of the sampling machine trolley and the second centroid;
s4, taking the central data of each set of the trolley aggregate of the sampling machine as a third mass center K3, taking the central data of each set of the trolley aggregate of the sampling machine as a fourth mass center K4, and comparing a third distance between K1 and K3 and a fourth distance between K2 and K4;
s5, when any one of the third distance and the fourth distance is larger than a preset threshold, repeating the steps S2-S4 until the third distance and the fourth distance are smaller than the preset threshold, and outputting a clustering boundary and an operation state;
s6, determining a derailment threshold based on the cluster boundary, and determining a derailment risk level based on the running state;
and S7, generating a derailment early warning model based on the derailment threshold value and the derailment risk level.
In this embodiment, clustering is a common unsupervised learning algorithm, that is, only data, no clear answer, that is, the training set has no label, the computer finds out the rule by itself, and places the samples with similar attributes in a group, and by performing clustering processing on the historical displacement data, the historical displacement data can be clustered into a plurality of cluster sets, wherein the number of the cluster sets of the trolley of the sampling machine is N1, and the number of the cluster sets of the trolley of the sampling machine is N2.
In this embodiment, the number of data selected from the history displacement data is identical to the initial cluster set number.
In this embodiment, the clustering result includes a clustering boundary and a running state, the clustering result is used to determine the derailment threshold, and the formal state is used to determine the derailment risk level.
In order to divide the derailment risk level of the derailment early warning model, in a specific embodiment of the present application, S6 determines a derailment threshold based on the cluster boundary, and determines the derailment risk level based on the running state, specifically:
determining a first risk threshold and a second risk threshold according to the clustering boundary, wherein the second risk threshold is larger than the first risk threshold;
when the historical displacement data in the clustered collection of the sampling machine cart and the sampling machine cart are the historical displacement data in normal operation, the derailment risk level is a low risk level;
when the historical displacement data in the clustered collection of the sampling machine cart and the sampling machine cart comprises the historical displacement data in normal operation and the historical displacement data in derailment, the derailment risk level is a medium risk level;
when the historical displacement data in the clustered collection of the sampling machine cart and the sampling machine cart are the historical displacement data in derailment, the derailment risk level is a high risk level.
Step S102, determining derailment risk levels of the sampling machine trolley and the sampling machine cart based on the model, wherein the derailment risk levels comprise a low risk level, a medium risk level and a high risk level.
In this embodiment, the risk levels are classified into a low risk level, a medium risk level and a high risk level, and those skilled in the art should understand that the above classification is only a specific classification manner of the present solution, and the risk levels can be adjusted according to actual needs.
In this embodiment, a low risk indicates no derailment risk, a medium risk indicates that the sampling machine cart or sampling machine trolley has a tendency to derail, and important monitoring can be performed, and a high risk indicates that the sampling machine cart or sampling machine trolley has a high possibility of derailing, and intervention adjustment is required.
In order to identify the derailment risk level of a sampling machine trolley or a sampling machine cart by a model, in a specific implementation of the application, the derailment risk level of the sampling machine trolley and the sampling machine cart is determined based on the model, specifically:
when the real-time running data of the wheels of the large sampling machine vehicle and the small sampling machine vehicle do not exceed a first risk threshold, the coal yard sampling machine system is determined to be in a low risk level;
when the actual running data of the wheels of the sampling machine cart does not exceed the first risk threshold value and the actual running data of the wheels of the sampling machine cart exceeds the first risk threshold value but does not exceed the second risk threshold value, the coal yard sampling machine system is determined to be in a medium risk level;
when the actual running data of the wheels of the sampling machine trolley does not exceed the first risk threshold value and the actual running data of the wheels of the sampling machine trolley does not exceed the first risk threshold value but does not exceed the second risk threshold value, the coal yard sampling machine system is determined to be in a medium risk level;
when the actual running data of the wheels of the sampling machine trolley and the sampling machine cart exceed the first risk threshold value but do not exceed the second risk threshold value, the coal yard sampling machine system is determined to be in a medium risk level;
when the actual running data of the wheels of the sampling machine cart exceeds a first risk threshold value but does not exceed a second risk threshold value and the actual running data of the wheels of the sampling machine cart exceeds the second risk threshold value, the coal yard sampling machine system is determined to be in a high risk level;
when the actual running data of the wheels of the sampling machine trolley exceeds the first risk threshold value but does not exceed the second risk threshold value and the actual running data of the wheels of the sampling machine trolley exceeds the second risk threshold value, the coal yard sampling machine system is determined to be in a high risk level;
and when the actual running data of the wheels of the sampling machine trolley and the sampling machine cart exceed the second risk threshold, the coal yard sampling machine system is determined to be in a high risk level.
In the embodiment, the actual running data of the wheels of the large sampling machine vehicle and the small sampling machine vehicle are specifically divided, different conditions of the large sampling machine vehicle and the small sampling machine vehicle in the running and sampling process are set, and the accuracy of a model early warning result and the safety of the sampling process are ensured.
And step 103, when the derailment risk level is not at a low risk level, identifying the derailment condition of the sampling machine trolley or the sampling machine trolley in real time based on the wheel vibration signals of the sampling machine trolley and the sampling machine trolley.
In this embodiment, when the derailment risk level is not low, it is necessary to further monitor the specific conditions of the large and small sample trucks, that is, whether derailment occurs.
Optionally, if any one of the sampling machine cart or the sampling machine trolley has a high risk level, the intervention adjustment can be directly performed without further judging whether derailment occurs, so that the safety of the sampling machine cart and the sampling machine trolley is ensured to the greatest extent.
In this embodiment, the derailment condition of the large and small trolley is determined by adopting the wheel vibration signals of the sampling trolley or the sampling trolley, and optionally, when only the risk level of the sampling trolley is high risk, only the derailment condition of the sampling trolley can be determined, and the same as the sampling trolley is adopted, so that the derailment condition determination process is reduced, and the determination efficiency is improved.
In order to identify the derailment condition of the sampling machine cart or the sampling machine cart, in some embodiments of the present application, when the derailment risk level is not at a low risk level, the derailment condition of the sampling machine cart or the sampling machine cart is identified in real time based on the wheel vibration signals of the sampling machine cart and the sampling machine cart, specifically:
acquiring the wheel vibration signal through a sensor arranged on a vehicle rail;
carrying out resonance demodulation on the wheel vibration signals to obtain demodulation signals, intercepting the resonance demodulation signals of a plurality of sleeper period time lengths by taking the time of obtaining the demodulation signals as a starting point, and obtaining the sample to be analyzed;
performing fast Fourier transform on the sample to be analyzed to obtain the corresponding frequency spectrum;
and when the characteristic spectral lines corresponding to sleeper intervals exist in the frequency spectrum, judging that the large or small sampling machine vehicle has derailment.
In this embodiment, the fast fourier transform in this step is a generic term of an efficient and fast computing method for computing the discrete fourier transform by using a computer, and by adopting this algorithm, the number of multiplications required for computing the discrete fourier transform by the computer can be greatly reduced, and especially, the more sampling points N are transformed, the more significant the saving of the computation amount of the fast fourier transform algorithm. That is to say, in this step, the sample to be analyzed is converted from the time domain signal to the frequency domain signal through the fast fourier transform, so as to obtain the spectrum corresponding to the sample to be analyzed, and when the characteristic spectral line corresponding to the sleeper interval exists in the spectrum, it is determined that the derailment condition exists in the large or small trolley of the sampling machine.
And step S104, when the trolley or the large trolley of the sampling machine is identified to be in a derailment state, an alarm signal is sent.
Optionally, an alarm may be installed on the sampling machine cart or the sampling machine trolley, and an alarm signal may also be sent to the controller to alert monitoring personnel.
Step S105, a safety isolation area is established based on the derailment condition of the sampling machine trolley or the sampling machine cart.
In this embodiment, when the derailment condition occurs, the safety isolation area is established according to the derailment condition of the vehicle, so as to ensure the safety of the sampling operation.
In order to set a safety isolation area, in some embodiments of the present application, derailment information of the sampling machine cart or the sampling machine cart is obtained, where the derailment information includes a vehicle number, a track number where the sampling machine cart is located, a derailment section, a traveling direction, and a traveling speed at the time of derailment;
and establishing a safety isolation area according to the running directions of the sampling machine cart and the track number and the derailment section.
In this embodiment, the derailment area can be rapidly located by the vehicle number and the track number, and the safety isolation area can be further determined according to the derailment section.
In order to set a safety isolation area, in some embodiments of the present application, a traveling direction of a sampling machine cart and a sampling machine cart is taken as a forward direction, and a track within a preset distance of the forward direction of the sampling machine cart and the sampling machine cart is set as a forward safety isolation area;
and taking the opposite direction of the traveling directions of the large sampling machine vehicle and the small sampling machine vehicle as the opposite direction, and setting the track within the preset distance of the opposite directions of the large sampling machine vehicle and the small sampling machine vehicle as an opposite safety isolation area.
In this embodiment, the regions within the front preset distance and the rear preset distance of the large and small sample-taking machine are used as the safety isolation regions.
To further increase the security of the sampling effort, in some embodiments of the present application, the method further comprises:
when the sampling machine cart or trolley is derailed, reporting the derailment information and the safety isolation area to a controller;
the safe isolation area is issued to other sampling machine carts and sampling machine carts through the controller;
when other sampling machine carts or sampling machine carts enter the safety isolation area, the sampling machine carts or sampling machine carts entering the safety isolation area are braked emergently through the controller.
In this embodiment, the information of the derailed vehicle is issued to other vehicles in the area, and when the other vehicles enter the safety isolation area, the safety of the other vehicles is ensured by timely emergency braking or giving an alarm.
To further improve the safety of the sampling work, in some embodiments of the present application, determining the derailment risk level of the sampling machine cart and the sampling machine cart based on the model further includes:
when the derailment risk level of the sampling machine trolley or the sampling machine cart is a high risk level, emergency braking is carried out on the sampling machine trolley or the sampling machine cart through a controller, and the machine is stopped for maintenance.
By applying the technical scheme, a derailment early warning model is established based on the historical displacement data of the sampling machine trolley and the sampling machine cart, wherein the historical displacement data comprises displacement information during normal running and displacement information during derailment state; determining derailment risk levels of the sampling machine trolley and the sampling machine trolley based on the model, wherein the derailment risk levels comprise a low risk level, a medium risk level and a high risk level; when the derailment risk level is not in a low risk level, identifying the derailment condition of the sampling machine trolley or the sampling machine trolley in real time based on wheel vibration signals of the sampling machine trolley and the sampling machine trolley; when the trolley of the sampling machine or the trolley of the sampling machine is identified to be in a derailment state, an alarm signal is sent; based on the derailment condition of the sampling machine trolley or the sampling machine cart, a safe isolation area is established, so that the derailment risk of the sampling machine trolley is timely pre-warned through a derailment pre-warning model, the derailment condition is accurately judged, an alarm signal is sent out when the derailment condition occurs, the derailment condition of the sampling machine can be accurately monitored, the safe isolation is timely carried out when the derailment occurs, and the safety and the sampling efficiency of the sampling work of the sampling machine are improved.
The embodiment of the application also provides a vehicle derailment early warning device, as shown in fig. 2, the device includes:
the model building module 10 is used for building a derailment early warning model based on the historical displacement data of the sampling machine trolley and the sampling machine cart, wherein the historical displacement data comprises displacement information during normal running and displacement information during derailment state;
a determining module 20, configured to determine, based on the model, a derailment risk level of the sampling locomotive and the sampling locomotive, where the derailment risk level includes a low risk level, a medium risk level, and a high risk level;
the identifying module 30 is configured to identify, in real time, a derailment condition of the sampling machine trolley or the sampling machine cart based on wheel vibration signals of the sampling machine trolley and the sampling machine cart when the derailment risk level is not at a low risk level;
an alarm module 40, configured to send an alarm signal when it is identified that the sampling machine trolley or the sampling machine cart is in a derailed state;
a setting up module 50 is configured to set up a safe isolation area based on the derailment condition of the sampling machine trolley or the sampling machine cart.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present application, and are not limiting thereof; although the present application has been described in detail with reference to the foregoing embodiments, one of ordinary skill in the art will appreciate that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not drive the essence of the corresponding technical solutions to depart from the spirit and scope of the technical solutions of the embodiments of the present application.
Claims (10)
1. The utility model provides a vehicle derailment early warning method which is characterized in that the method is applied to a coal yard sampling machine system comprising a sampling machine trolley and a sampling machine cart, wherein the sampling machine trolley and the sampling machine cart are mutually connected and run according to a preset track, and the method comprises the following steps:
establishing a derailment early warning model based on historical displacement data of the sampling machine trolley and the sampling machine cart, wherein the historical displacement data comprises displacement information during normal running and displacement information during derailment state;
determining derailment risk levels of the sampling machine trolley and the sampling machine trolley based on the model, wherein the derailment risk levels comprise a low risk level, a medium risk level and a high risk level;
when the derailment risk level is not in a low risk level, identifying the derailment condition of the sampling machine trolley or the sampling machine trolley in real time based on wheel vibration signals of the sampling machine trolley and the sampling machine trolley;
when the trolley of the sampling machine or the trolley of the sampling machine is identified to be in a derailment state, an alarm signal is sent;
and establishing a safe isolation area based on the derailment condition of the sampling machine trolley or the sampling machine cart.
2. The method according to claim 1, wherein the derailment early warning model is established based on the historical displacement data of the sampling locomotive trolley and the sampling locomotive cart, specifically:
s1, clustering the acquired historical displacement data of the sampling machine trolley and the sampling machine cart respectively to obtain the number N1 of clustering sets of the sampling machine trolley and the number N2 of clustering sets of the sampling machine cart;
s2, selecting N1 data from historical displacement data of the trolley of the sampling machine as a first centroid K1, and selecting N1 data from historical displacement data of the trolley of the sampling machine as a second centroid K2;
s3, clustering each historical displacement data of the sampling machine trolley to a set to which a first centroid corresponding to a first distance belongs to obtain N1 aggregated sets, and clustering each historical displacement data of the sampling machine trolley to a set to which a second centroid corresponding to a second distance belongs to obtain N2 aggregated sets, wherein the first distance is the minimum distance between each historical displacement data of the sampling machine trolley and the first centroid, and the second distance is the minimum distance between each historical displacement data of the sampling machine trolley and the second centroid;
s4, taking the central data of each set of the trolley aggregate of the sampling machine as a third mass center K3, taking the central data of each set of the trolley aggregate of the sampling machine as a fourth mass center K4, and comparing a third distance between K1 and K3 and a fourth distance between K2 and K4;
s5, when any one of the third distance and the fourth distance is larger than a preset threshold, repeating the steps S2-S4 until the third distance and the fourth distance are smaller than the preset threshold, and outputting a clustering boundary and an operation state;
s6, determining a derailment threshold based on the cluster boundary, and determining a derailment risk level based on the running state;
and S7, generating a derailment early warning model based on the derailment threshold value and the derailment risk level.
3. The method according to claim 1, characterized in that the derailment risk level of the sampling trolley and the sampling trolley is determined based on the model, in particular:
when the real-time running data of the wheels of the large sampling machine vehicle and the small sampling machine vehicle do not exceed a first risk threshold, the coal yard sampling machine system is determined to be in a low risk level;
when the actual running data of the wheels of the sampling machine cart does not exceed the first risk threshold value and the actual running data of the wheels of the sampling machine cart exceeds the first risk threshold value but does not exceed the second risk threshold value, the coal yard sampling machine system is determined to be in a medium risk level;
when the actual running data of the wheels of the sampling machine trolley does not exceed the first risk threshold value and the actual running data of the wheels of the sampling machine trolley does not exceed the first risk threshold value but does not exceed the second risk threshold value, the coal yard sampling machine system is determined to be in a medium risk level;
when the actual running data of the wheels of the sampling machine trolley and the sampling machine cart exceed the first risk threshold value but do not exceed the second risk threshold value, the coal yard sampling machine system is determined to be in a medium risk level;
when the actual running data of the wheels of the sampling machine cart exceeds a first risk threshold value but does not exceed a second risk threshold value and the actual running data of the wheels of the sampling machine cart exceeds the second risk threshold value, the coal yard sampling machine system is determined to be in a high risk level;
when the actual running data of the wheels of the sampling machine trolley exceeds the first risk threshold value but does not exceed the second risk threshold value and the actual running data of the wheels of the sampling machine trolley exceeds the second risk threshold value, the coal yard sampling machine system is determined to be in a high risk level;
and when the actual running data of the wheels of the sampling machine trolley and the sampling machine cart exceed the second risk threshold, the coal yard sampling machine system is determined to be in a high risk level.
4. The method according to claim 1, characterized in that S6 a derailment threshold is determined based on the cluster boundary, and a derailment risk level is determined based on the operating state, in particular:
determining a first risk threshold and a second risk threshold according to the clustering boundary, wherein the second risk threshold is larger than the first risk threshold;
when the historical displacement data in the clustered collection of the sampling machine cart and the sampling machine cart are the historical displacement data in normal operation, the derailment risk level is a low risk level;
when the historical displacement data in the clustered collection of the sampling machine cart and the sampling machine cart comprises the historical displacement data in normal operation and the historical displacement data in derailment, the derailment risk level is a medium risk level;
when the historical displacement data in the clustered collection of the sampling machine cart and the sampling machine cart are the historical displacement data in derailment, the derailment risk level is a high risk level.
5. The method according to claim 1, characterized in that when the derailment risk level is not at a low risk level, based on the wheel vibration signals of the sampling trolley and the sampling trolley, and based on the vibration signals, identifying in real time the derailment condition of the sampling trolley or the trolley, in particular:
acquiring the wheel vibration signal through a sensor arranged on a vehicle rail;
carrying out resonance demodulation on the wheel vibration signals to obtain demodulation signals, intercepting the resonance demodulation signals of a plurality of sleeper period time lengths by taking the time of obtaining the demodulation signals as a starting point, and obtaining the sample to be analyzed;
performing fast Fourier transform on the sample to be analyzed to obtain the corresponding frequency spectrum;
and when the characteristic spectral lines corresponding to sleeper intervals exist in the frequency spectrum, judging that the large or small sampling machine vehicle has derailment.
6. The method according to claim 1, characterized in that a safety isolation area is established based on the derailment of the sampling trolley or sampling trolley, in particular:
acquiring derailment information of the sampling machine cart or the sampling machine cart, wherein the derailment information comprises a vehicle number, a track number, a derailment section, a running direction and a running speed during derailment;
and establishing a safety isolation area according to the running directions of the sampling machine cart and the track number and the derailment section.
7. The method according to claim 1, wherein a safety isolation area is established according to the traveling direction of the sampling machine cart and the track number and the derailment section, specifically:
taking the running directions of the large sampling machine vehicle and the small sampling machine vehicle as forward directions, and setting the tracks within a preset distance of the forward directions of the large sampling machine vehicle and the small sampling machine vehicle as forward safe isolation areas;
and taking the opposite direction of the traveling directions of the large sampling machine vehicle and the small sampling machine vehicle as the opposite direction, and setting the track within the preset distance of the opposite directions of the large sampling machine vehicle and the small sampling machine vehicle as an opposite safety isolation area.
8. The method according to claims 6-7, characterized in that the method further comprises:
when the sampling machine cart or trolley is derailed, reporting the derailment information and the safety isolation area to a controller;
the safe isolation area is issued to other sampling machine carts and sampling machine carts through the controller;
when other sampling machine carts or sampling machine carts enter the safety isolation area, the sampling machine carts or sampling machine carts entering the safety isolation area are braked emergently through the controller.
9. The method of claim 1, wherein determining a derailment risk level for the sampling locomotive and the sampling locomotive based on the model further comprises:
when the derailment risk level of the sampling machine trolley or the sampling machine cart is a high risk level, emergency braking is carried out on the sampling machine trolley or the sampling machine cart through a controller, and the machine is stopped for maintenance.
10. The utility model provides a vehicle derailment early warning equipment, its characterized in that, equipment is applied to the coal yard sampling machine system including sampling machine dolly and sampling machine cart, sampling machine dolly and sampling machine cart interconnect and travel according to predetermined track, the equipment includes:
the model building module is used for building a derailment early warning model based on the historical displacement data of the sampling machine trolley and the sampling machine cart, wherein the historical displacement data comprises displacement information during normal running and displacement information during a derailment state;
the determining module is used for determining derailment risk levels of the sampling machine trolley and the sampling machine cart based on the model, wherein the derailment risk levels comprise a low risk level, a medium risk level and a high risk level;
the identifying module is used for identifying the derailment condition of the sampling machine trolley or the sampling machine trolley in real time based on the wheel vibration signals of the sampling machine trolley and the sampling machine trolley when the derailment risk level is not at the low risk level;
the alarm module is used for sending an alarm signal when the sampling machine trolley or the sampling machine cart is identified to be in a derailment state;
the establishing module is used for establishing a safe isolation area based on the derailment condition of the sampling machine trolley or the sampling machine cart.
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| CN202310316500.5A CN116304759A (en) | 2023-03-28 | 2023-03-28 | Vehicle derailment early warning method and device |
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| CN202310316500.5A CN116304759A (en) | 2023-03-28 | 2023-03-28 | Vehicle derailment early warning method and device |
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